Method and system for influencing auction based advertising opportunities based on user characteristics

ABSTRACT

Disclosed is a computer implemented method of bidding for advertisement opportunities based on user behavior data. The computer implemented method may include receiving a primary bid value associated with an audience list including a plurality of users. Further, each user of the plurality of users may be associated with user behavior data corresponding to a primary filtering criteria. Further, the computer implemented method may include receiving a secondary bid value associated with a sub-audience list including one or more users. Further, the one or more users may be associated with user behavior data corresponding to a secondary filtering criteria. Additionally, the computer implemented method may include transmitting a bid for an advertisement opportunity based on each of the primary bid value and the secondary bid value. The advertisement may be presentable to the one or more users.

RELATED APPLICATIONS

This is a continuation application of U.S. application Ser. No.15/177,204 filed Jun. 8, 2016, entitled “Method and System forInfluencing Auction Based Advertising Opportunities Based on Usercharacteristics,”, which Applicant claims the benefit under theprovisions of 35 U.S.C. § 119(e), U.S. Provisional Application No.62/173,071, filed Jun. 9, 2015, which are incorporated herein byreference.

The following related U. S. patent applications, filed on even dateherewith in the name of Clickagy, LLC, assigned to the assignee of thepresent application, are hereby incorporated by reference:

-   -   Attorney Docket No. 00279.005-PA-USN-86R, entitled “METHOD,        SYSTEM AND COMPUTER READABLE MEDIUM FOR CREATING A PROFILE OF A        USER BASED ON USER BEHAVIOR;”    -   Attorney Docket No. 00279.006-PA-USN-86R, entitled “METHOD AND        SYSTEM FOR PROVIDING BUSINESS INTELLIGENCE BASED ON USER        BEHAVIOR;” and    -   Attorney Docket No. 00279.007-PA-USN-86R, entitled “METHOD AND        SYSTEM FOR CREATING AN AUDIENCE LIST BASED ON USER BEHAVIOR        DATA.”

It is intended that each of the referenced applications may beapplicable to the concepts and embodiments disclosed herein, even ifsuch concepts and embodiments are disclosed in the referencedapplications with different limitations and configurations and describedusing different examples and terminology.

FIELD OF DISCLOSURE

The present disclosure generally relates to bidding of advertisementopportunities based on user characteristics. More specifically, thepresent disclosure relates to a method and system for adjusting biddingof advertisement opportunities based on user characteristics.

BACKGROUND

Advertisers are on a constant endeavor to provide relevant informationregarding products and/or services to users who may be interested inpurchasing those products and/or services. Advertisements may bepresented to users through various mediums. For instance, the displaydevice located at public places may be used to visually presentadvertisements to users in the vicinity of the display device.Similarly, advertisements are generally presented communication channelssuch as radio, television and audio video players.

Most of the advertisements presented to users do not take into accountcharacteristics of the users viewing the advertisements. Accordingly,users may not always be exposed to advertisements relevant to theirinterests. For example, advertisements presented on television aregenerally independent of interests of users viewing the advertisement.As a result, a conversion rate of the advertisement presentedindependent of user interests is very poor. In other words, suchadvertisements which do not take into account the interests of users areineffective.

Accordingly, several advertisers are beginning to move towards atargeted advertising paradigm where advertisements are presented tousers according to user interests. For example, users may be monitoredwhile browsing the Internet and a set of user interests may beidentified. Accordingly, based on the set of user interests, relevantadvertisements of products and/or services may be identified andpresented to users. Such advertisements are commonly known as targetedadvertisements.

However, privacy of users is a major challenge posing implementation ofeffective targeted advertising. In order to be able to provideadvertisements relevant to users, user interests need to be identifiedat a granular level. This entails extensive monitoring of user activity,such as for example, online browsing. Further, there are several laws inmost countries protecting privacy of users by forbidding collectionand/or dissemination of personal information.

As a result, advertisers face difficulty in obtaining sufficient dataregarding user behavior in order to identify user interests moreaccurately. Accordingly, although existing targeted advertisements maybe more effective than non-targeted advertisements, there is much scopefor improvement in effectiveness. Therefore, there is a need for methodsand systems for providing targeted advertisements with a greater degreeof effectiveness.

Further, several methods and systems of advertising online are based ona bidding model where different advertisers may compete to present theirrespective advertisements at a given advertisement opportunity. Forexample, a publisher of online content may have an advertisement spaceon a web page where an advertisement may be displayed. Accordingly, thepublisher may invite bids from multiple advertisers to present anadvertisement in the advertising space. Further, an advertiser proposingthe maximum bid amount may be considered a winner. Accordingly, theadvertiser may be allowed to display a chosen advertisement in theadvertising space. Such a model is beneficial to both the publisher andthe advertisers since the publisher is able to maximally monetize theadvertising space while the advertisers are able to control and limittheir advertising budget according to their needs.

Generally, the bid for an advertising space depends on a contextcorresponding to the advertising space. For example, a web pagecontaining the advertising space may be related to a particular topicsuch as, for example, sports. Accordingly, it may be inferred that usersviewing the web page may be interested in sports products such as,shoes. Accordingly, the publisher may notify advertisers of anadvertising context, such as through a keyword “shoes”. On the otherhand, an advertiser may be willing advertise a particular brand of shoeswithin the advertising space. Accordingly, the advertiser may place aspecific bid amount for the keyword “shoes”. As a result, anyadvertisement opportunity having the keyword “shoes” may be a relevantadvertising opportunity for the advertiser.

However, presenting such targeted advertisements to users based onbidding may not be effective in relation to advertiser. For instance,such a technique of advertising based on bidding assumes that the entirepool of users viewing the web page is homogeneous. In other words, theadvertiser in existing bid based techniques, competes with the same bidamount for each user within the pool of users. However, it is evidentthat they are significant differences between users within the pool withregard to user interests and/or affinity towards a product, service or abrand. Accordingly, the advertisers may be disadvantaged in competingfor an advertisement presented to the pool of users while a conversionrate of the advertisement may vary drastically across users owing to theheterogeneity of user interests within the pool of users. Accordingly,there is a need for improved methods and systems for managing bidding ofadvertisement spaces while taking into account differences between userswith regard to interests and/or affinities.

BRIEF OVERVIEW

A bidding platform may be provided. This brief overview is provided tointroduce a selection of concepts in a simplified form that are furtherdescribed below in the Detailed Description. This brief overview is notintended to identify key features or essential features of the claimedsubject matter. Nor is this brief overview intended to be used to limitthe claimed subject matter's scope.

Disclosed are methods and systems for facilitating bidding ofadvertisement spaces. According to some embodiments, an improved methodand system may be provided in order to facilitate advertisers to placebids on advertisements presentable to users according to interests ofthe users. In other words, advertisers may be enabled to bid foradvertisement opportunities based on user behavior data. For example,advertisers may be allowed to specify a set of users in terms of userbehavior data such as, for example, webpages visited by users, one ormore interests expressed either implicitly and/or explicitly by theusers, and so on. Accordingly, an advertiser may specify a particularbid amount for an advertisement opportunity in relation tocharacteristics of the user viewing the advertisement.

Further, in some embodiments, advertisers may be enabled to specify aplurality of big amounts corresponding to a plurality of sets of userssatisfying a plurality of criteria based on user behavior data. Forinstance, a primary criteria based on user behavior data may include aninterest towards shoes. Accordingly, a primary set of users may beidentified based on past user behavior data indicative of an explicitand/or an implicit interest of the users in shoes. For example, onlinebrowsing by users may be monitored and those users who visited webpagesrelated to shoes may be identified as the primary set of users.Accordingly, an advertiser may specify a primary bid amountcorresponding to the primary set of users for an advertisementopportunity related to shoes. As a result, an association between theprimary set of users and the primary bid amount may be created andstored. Accordingly, when an advertisement opportunity occurs, forexample, when a user of the primary set of users is viewing a webpage,the advertiser may bid for an advertisement space on the webpage withthe primary bid amount. If the primary bid amount wins the bid, theadvertiser may present a desired advertisement to the user.

Further, the advertiser may also be enabled to specify a secondarycriteria based on user behavior data, such as, for example, an interesttowards sports shoes. Accordingly, a secondary set of users may beidentified based on past user behavior data indicative of an explicitand/or an implicit interest of the users in sports shoes. Further, insome instances, the secondary set of users may be a subset of theprimary set of users. For example, online browsing by users may bemonitored and those users who visited webpages related to sports shoesmay be identified as the secondary set of users. Accordingly, anadvertiser may specify a secondary bid amount corresponding to thesecondary set of users for an advertisement opportunity related tosports shoes. As a result, an association between the secondary set ofusers and the secondary bid amount may be created and stored.Accordingly, when an advertisement opportunity occurs, for example, whena user of the second set of users is viewing a webpage, the advertisermay bid for an advertisement space on the webpage with the secondary bidamount. In some instances, the secondary bid amount may be greater thanthe primary bid amount. If the secondary bid amount wins the bid, theadvertiser may present a desired advertisement relating to sports shoesto the user.

As a result, the advertiser may be enabled to prefer one set of usersover others while competing to bid for presenting advertisements. Forinstance, an advertiser of sports shoes may place higher bids foradvertising to users whose behavior data indicates a specific interesttowards sport shoes as opposed to other users whose behavior dataindicates a general interest towards shoes.

Furthermore, in some embodiments, the advertisers may also be enabled tocontrol bidding based on a context corresponding to the advertisementopportunity. Accordingly, advertisers may be enabled to specify one ormore contextual conditions under which a bid for an advertisementopportunity may be placed. For instance, further to identifying arelevant user currently viewing a webpage, information regardingcontents of the webpage may be specified as a contextual condition.Accordingly, an advertisement may be presented to the user providedthat, for example, the content of the webpage is relevant to theadvertisement. For instance, a user viewing a sports article may beidentified as part of the secondary set of users. Further, since thecontext of the webpage relates to sports, a bid to present anadvertisement related to sports shoes may be made. As a result, whenpresented, the likelihood of the advertisement being noticed and actedupon by the user may increase.

Both the foregoing brief overview and the following detailed descriptionprovide examples and are explanatory only. Accordingly, the foregoingbrief overview and the following detailed description should not beconsidered to be restrictive. Further, features or variations may beprovided in addition to those set forth herein. For example, embodimentsmay be directed to various feature combinations and sub-combinationsdescribed in the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this disclosure, illustrate various embodiments of the presentdisclosure. The drawings contain representations of various trademarksand copyrights owned by the Applicants. In addition, the drawings maycontain other marks owned by third parties and are being used forillustrative purposes only. All rights to various trademarks andcopyrights represented herein, except those belonging to theirrespective owners, are vested in and the property of the Applicants. TheApplicants retain and reserve all rights in their trademarks andcopyrights included herein, and grant permission to reproduce thematerial only in connection with reproduction of the granted patent andfor no other purpose.

Furthermore, the drawings may contain text or captions that may explaincertain embodiments of the present disclosure. This text is included forillustrative, non-limiting, explanatory purposes of certain embodimentsdetailed in the present disclosure. In the drawings:

FIG. 1 illustrates a block diagram of an operating environmentconsistent with the present disclosure;

FIG. 2 is a flow chart of a method of creating user profiles based onuser behavior data;

FIG. 3 illustrates an example of how logic functions may sort specificgroups;

FIG. 4 illustrates and example of how such logic may be used to provideoptimally targeted advertisements;

FIG. 5 illustrates a flow chart of a method of bidding for advertisementopportunities in accordance with some embodiments;

FIG. 6A illustrates a flow chart of a method of facilitating creation ofan audience list in accordance with some embodiments;

FIG. 6B illustrates a flow chart of a method of facilitating creation ofa sub-audience list in accordance with some embodiments;

FIG. 7 illustrates a flow chart of a method of bidding for advertisementopportunities based on user identifiers in accordance with someembodiments;

FIG. 8 illustrates a flow chart of a method of bidding for advertisementopportunities based on a contextual variable in accordance with someembodiments;

FIG. 9 illustrates an exemplary user interface for receiving bidadjustment based on user behavior data in accordance with someembodiments;

FIG. 10 illustrates an exemplary user interface for selecting a bidadjustment parameter in accordance with some embodiments;

FIG. 11 illustrates an exemplary user interface for receiving bidadjustment based on user behavior data in accordance with someembodiments;

FIG. 12 illustrates a method of bidding for advertisement opportunitiesbased on user behavior in accordance with some embodiments.

FIG. 13 illustrates an online user behavior of a user based on which auser profile may be created in accordance with some embodiments;

FIG. 14 illustrates an exemplary comprehensive user browsing data basedon which a user profile may be created in accordance with someembodiments;

FIG. 15 illustrates Natural Language Processing performed on dataextracted from webpages visited by a user based on which a user profilemay be created in accordance with some embodiments; and

FIG. 16 is a block diagram of a system including a computing device forperforming the methods of FIG. 2, FIG. 5 to FIG. 8 and FIG. 12

DETAILED DESCRIPTION

As a preliminary matter, it will readily be understood by one havingordinary skill in the relevant art that the present disclosure has broadutility and application. As should be understood, any embodiment mayincorporate only one or a plurality of the above-disclosed aspects ofthe disclosure and may further incorporate only one or a plurality ofthe above-disclosed features. Furthermore, any embodiment discussed andidentified as being “preferred” is considered to be part of a best modecontemplated for carrying out the embodiments of the present disclosure.Other embodiments also may be discussed for additional illustrativepurposes in providing a full and enabling disclosure. As should beunderstood, any embodiment may incorporate only one or a plurality ofthe above-disclosed aspects of the display and may further incorporateonly one or a plurality of the above-disclosed features. Moreover, manyembodiments, such as adaptations, variations, modifications, andequivalent arrangements, will be implicitly disclosed by the embodimentsdescribed herein and fall within the scope of the present disclosure.

Accordingly, while embodiments are described herein in detail inrelation to one or more embodiments, it is to be understood that thisdisclosure is illustrative and exemplary of the present disclosure, andare made merely for the purposes of providing a full and enablingdisclosure. The detailed disclosure herein of one or more embodiments isnot intended, nor is to be construed, to limit the scope of patentprotection afforded in any claim of a patent issuing here from, whichscope is to be defined by the claims and the equivalents thereof. It isnot intended that the scope of patent protection be defined by readinginto any claim a limitation found herein that does not explicitly appearin the claim itself.

Thus, for example, any sequence(s) and/or temporal order of steps ofvarious processes or methods that are described herein are illustrativeand not restrictive. Accordingly, it should be understood that, althoughsteps of various processes or methods may be shown and described asbeing in a sequence or temporal order, the steps of any such processesor methods are not limited to being carried out in any particularsequence or order, absent an indication otherwise. Indeed, the steps insuch processes or methods generally may be carried out in variousdifferent sequences and orders while still falling within the scope ofthe present invention. Accordingly, it is intended that the scope ofpatent protection is to be defined by the issued claim(s) rather thanthe description set forth herein.

Additionally, it is important to note that each term used herein refersto that which an ordinary artisan would understand such term to meanbased on the contextual use of such term herein. To the extent that themeaning of a term used herein—as understood by the ordinary artisanbased on the contextual use of such term—differs in any way from anyparticular dictionary definition of such term, it is intended that themeaning of the term as understood by the ordinary artisan shouldprevail.

Regarding applicability of 35 U.S.C. § 112, ¶6, no claim element isintended to be read in accordance with this statutory provision unlessthe explicit phrase “means for” or “step for” is actually used in suchclaim element, whereupon this statutory provision is intended to applyin the interpretation of such claim element.

Furthermore, it is important to note that, as used herein, “a” and “an”each generally denotes “at least one,” but does not exclude a pluralityunless the contextual use dictates otherwise. When used herein to join alist of items, “or” denotes “at least one of the items,” but does notexclude a plurality of items of the list. Finally, when used herein tojoin a list of items, “and” denotes “all of the items of the list.”

The following detailed description refers to the accompanying drawings.Wherever possible, the same reference numbers are used in the drawingsand the following description to refer to the same or similar elements.While many embodiments of the disclosure may be described,modifications, adaptations, and other implementations are possible. Forexample, substitutions, additions, or modifications may be made to theelements illustrated in the drawings, and the methods described hereinmay be modified by substituting, reordering, or adding stages to thedisclosed methods. Accordingly, the following detailed description doesnot limit the disclosure. Instead, the proper scope of the disclosure isdefined by the appended claims. The present disclosure contains headers.It should be understood that these headers are used as references andare not to be construed as limiting upon the subjected matter disclosedunder the header.

The present disclosure includes many aspects and features. Moreover,while many aspects and features relate to, and are described in, thecontext of data mining for marketing purposes, embodiments of thepresent disclosure are not limited to use only in this context. Forexample, the platform may be used to study demographics, psychographics,market behavior, competitor affinity, and expanding markets.

I. Platform Overview Consistent with embodiments of the presentdisclosure, a bidding platform may be provided. This overview isprovided to introduce a selection of concepts in a simplified form thatare further described below. This overview is not intended to identifykey features or essential features of the claimed subject matter. Nor isthis overview intended to be used to limit the claimed subject matter'sscope.

A platform consistent with embodiments of the present disclosure may beused by individuals or companies to perform bidding of advertisingopportunities for presenting advertisements to users characterized byone or more interests.

Accordingly, the present disclosure provides a platform that enablesclients such as, for example, advertisers to create an audience list andplace bids on advertisements targeting individuals in the audience List.Additionally, the platform also allows the advertisers to adjust howheavily those bids are placed for different users in the audience list.

In order to create the audience list, the advertiser may specify userbehavior data in the form of a filtering criteria. More particularly,the advertiser may specify users to be targeted based on a type ofonline behavior exhibited by the users. For example, the filteringcriteria may specify webpages visited, keywords associated with thevisited webpages, affinities/importance of the keywords and so on. Inother words, the advertiser may specify certain user behavior that maybe indicative of interests in one or more topics, products, servicesetc. Accordingly, users who have exhibited such behavior may beidentified and targeted.

Additionally, a filtering criteria based on user behavior may also beused to adjust how heavily each user in the audience list gets targeted.

For example, subsequent to setting up the audience list for bidding oncertain online advertisements, the client may control how heavily thebids will be placed to win an advertising space to relevant individualson relevant web pages. For instance, the audience list may includeindividuals who may be the target for a corvette. Further, the clientmay adjust how heavily the bids may be placed on each of those targetedindividuals. For example, the client may add a bid adjustment parameterbased on Domain List and specify the domain to be ‘www.autotrader.com’as exemplarily illustrated in FIG. 9. Accordingly, bids corresponding toindividuals in the audience List who have visited autotrader.com may beincreased to by, for example, 200%.

Further, the client may be enabled to provide a number of such filteringcriteria in order to characterize a preferred user behavior.Accordingly, the client may be presented a user interface, asexemplarily illustrated in FIG. 10 in order to specify multiplefiltering criteria. For example, the filtering criteria may be based ona plurality of parameters including, but not limited to, IP address,Segment, Keyword, Domain, Page URL, Continent, Country, Region, City,Zip code, Hyperlocal, ISP, Connection type, Device type, Browser,Operating System, Screen dimensions, Browser dimensions, language etc.Further, the client may be enabled to combine as many of theseparameters as necessary to determine bid adjustments.

Additionally, in some embodiments, the platform may also enable theclients to specify a blacklist of user behavior, correspondingindividuals and/or a context of the advertising opportunity.Accordingly, in such cases, the client may reduce the bid by 100% ifcertain parameters are met. For example, if the in a targeted individualin the audience list is currently on a violent domain and the client isDisney, the client may not want their brand to be affiliated with aviolent website. Accordingly, the client may set the bid adjustment tobe −100% as exemplarily illustrated in FIG. 16.

Further, in some instances, the client may also be enabled to make bidadjustments based on one or more conditions such as, for example, timeof day.

Accordingly, performance of retargeting may be improved by varying theretargeting process in time. For instance, once a user leaves a client'ssite, there are moments when the user may be focused on something elseand don't want to be bothered, and other times the user may be open toretargeting and willing to convert. An ideal time is when the userreturns to the client's market on their own will, casually researchingcompetitors and exploring options. Accordingly, when the user decides toresume shopping on their own, the client's retargeting may improve byhyper-aggressively bidding for displaying advertisement to the user,driving the user home in the critical final hour.

For example, as illustrated in FIG. 12, at step 1, the platform maylightly retarget the user across normal browsing. Accordingly, when theuser visits general websites such as a news website, a weather websiteetc., the platform may place a nominal bid amount for presenting theclient's advertisement to the user. However, at step 2, the platform mayreceive a notification of the user currently browsing a webpage relevantto the industry of the client. Accordingly, at step 3, the platform mayplace higher bid amounts for presenting the client's advertisement tothe user. As a result, at step 4, the user may be more receptive to theclient's advertisement driving better engagement and conversion rate.

Similarly, when the user has previously visited the client's website,the client may wish to retarget the user on other websites with anominal bid amount. However, when the user is on, for example, acompetitor's website, the client may want to increase the bid amount toensure that the client's advertisement appears on the competitor's site.Accordingly, the platform may receive such bid adjustment conditionsfrom the client and place bids accordingly.

As another example, the platform may enable the client to create anaudience list of users who exhibited interest in a particular type ofcar. Subsequently, the platform may allow the clients to specify bidadjusts to a specific set of users within the audience list. Forinstance, the client, such as for example, Corvette, may increase bids(or adjust bids) on users who have been shopping on competitor websites.Accordingly, the client may be enabled to specific the competitorwebsites. Alternatively and/or additionally, the client may also specifya particular region, time of day, ISP, Browser type, domain name,keywords, user affinity and so on.

Additionally, in some embodiments, the client may be enabled to specifya subset of the audience list by performing logical operations between aplurality of existing audience lists. For example, a user interface maybe provided to the client in order to create a subset of the audiencelist by dragging and dropping audience lists into a workspace area andperforming logical operations such as “AND”, “NOT”, “OR” and so on.Further, the client may be enabled to directly manipulate graphicalobjects such as circles representing audience lists. Accordingly, byoverlapping two graphical objects, an intersection of the audience listscorresponding to the graphical objects may be created, such as in a Venndiagram. Based on the intersection, the subset of the audience list maybe identified.

Additionally, in some embodiments, the platform may enable the client tospecify a time-based budget allocation. For instance, advertisementagencies allow advertisers to specify the times during which theadvertisers wish to serve the advertisements. Accordingly, the platformmay enable dynamic allocation of the budget over time.

Further, an exemplary application of the methods and systems disclosedherein, may include performing bid adjustments at a Demand Side Platform(DSP), such as an ad buying/bidding system, to make bidding moreefficient. For example, a DSP may have bought a list of a million peoplewho have been identified as being in-market for widgets. Conventionally,a bidder may bid a flat bid amount, for example $4.00 CPM, to servewidget ads to individuals in the list regardless of the website thatthey individuals may be visiting. In contrast, in accordance withmethods and systems disclosed herein, bid adjustments may be made toimprove the performance by adding another layer of logic on top of thebidding process.

For example, subsequent to running an audience targeting campaign for aweek, reports may indicate that when individuals are visiting a websiteabout golf, the conversion rate of the ads is twice as high incomparison to when the individuals are visiting www.answers.com.Accordingly, the DSP add a bid adjustment, such as for example, 150%, incase the ad opportunity is relevant to golf (contextual keyword presenton the webpage). As a result, the bid amount may increase from $4.00 to$10.00 CPM. Further, bid amounts for presenting ads on www.answers.commay be reduced by a predetermined percentage value, such as −90%,lowering the bid amount of $0.40 CPM. Further, in some instances,multiple bid adjustments may be combined together according to apredetermined combining function. For example, if an individual in theaudience targeting campaign visits a golf related webpage onwww.answers.com, the DSP may bid $1.00 CPM ($4.00×1.5×0.1).

Similarly, as another example, marketers may target individuals using atype of computers, such as Macs, over others who may be using Windows.Accordingly, the marketers may specify a +20% adjustment of bids to bemade if an individual is using “OS X”. Likewise, further such bidadjustment specifications may be specified as follows: Using asmartphone: −15%; located in Texas: +25%; also present in anotheraudience list: +120%; currently a Sunday: +50%; Using Comcast Internetconnection: −10%; Gender is male: −80% and so on. Accordingly, bidamounts may be dynamically adjusted accordingly to desirablecharacteristics of individuals in order to achieve higher conversionrates.

Embodiments of the present disclosure may operate in a plurality ofdifferent environments. For example, in a first aspect, the platform mayreceive notice that an individual has visited a webpage. Then, theplatform may crawl that page to gather raw data from the page. Forexample, the platform may use various algorithms, including, but notlimited to, for example, natural language processing (NLP) and digitalsignal processing (audio/image/video data) to search the web page forkey words or phrases.

Still consistent with embodiments of the present disclosure, theplatform may receive raw data as it tracks individuals throughout, forexample, an ad network or collection of ad networks. Tracking mayinclude, for example, but not be limited to, a crawling of each visitedwebpage so as to create a profile for the page. As will be furtherdetailed below, the profile may be generated by, for example, theaforementioned algorithms used to gather raw data for the page.

Accordingly, in some embodiments, interaction of a user with a pluralityof servers, such as for example, content servers, ad servers and so onmay be monitored. For instance, when the user visits a webpage providedby a server, a tracking cookie may be instantiated in order to saveinformation regarding the user and/or the user's interaction with thewebpage. For instance, the tracking cookie may be instantiated at theserver side and may include information such as a timestampcorresponding to the user's visiting of the webpage and one or moreidentifiers associated with the user. The one or more identifiers may befor example, a network identifier such as an Internet Protocol (IP)number and/or a MAC number, a device identifier such as an IMEI number,a software environment identifier, such as OS name, browser name etc.,user identifiers such as email address, first name, last name, middlename, postal address etc. and values of contextual variables such as GPSlocation of the device used to access the webpage, sensor readings ofthe device while accessing the webpage and so on.

In some embodiments, the one or more identifiers, such as the IMEInumber, may uniquely identify the user while preserving anonymity of theuser.

In other embodiments, the one or more identifiers may be subjected toencryption or a one way hashing in order to render the one or moreidentifiers unreadable to other users while maintaining the ability ofthe one or more identifiers to uniquely identify the user. For example,in some instances, tracking cookie may be instantiated on a client side,where the tracking cookie may reside on a user device, such as asmartphone or a laptop computer. Accordingly, any information collectedby the tracking cookie may remain accessible in human readable form onlywithin the user device. However, prior to transmitting the trackingcookie to the server side, the information collected may be subjected tohashing. Accordingly, in some embodiments, information about the user inhuman readable form may not be available at the server side. Thus, usersmay be ensured of preserving their privacy.

Further, in some embodiments, each of the plurality of servers may adopta common hashing algorithm such that each of the plurality of serversmay compute a common hash value for the one or more identifiers.Accordingly, when information in the tracking cookies from each of theplurality of servers is transmitted to the platform, the informationcollected by multiple tracking cookies may be identified as beingassociated with the same user based on the common hash value. Such atechnique may allow tracking the user across multiple servers accessedby the user through a common user device.

In yet further embodiments of the present disclosure, the raw data maybe from purchased data acquired by data aggregators. The raw data mayinclude, for example, a plurality of device specific information (e.g.,device serial number, IP address, and the like) along with a listing ofwebsites accessed by the device. The platform may be enabled to identifya plurality of devices associated with a single individual and,subsequently, associated the data aggregated and processed for eachdevice to a single individual profile.

For instance, in some embodiments, where the user may access the sameand/or different servers through multiple user devices, a correlation ofthe information collected by the multiple cookies may be performed inorder to track the user. For instance, each of the multiple trackingcookies may not include all of the one or more identifiers. For example,the user may access a webpage of a server using a smartphone, while theuser may access a webpage of another server using a laptop computer atwork. Further, the laptop computer may include additional restrictionsthat forbid the tracking cookie from collecting some of the one or moreidentifiers. However, at least some of the information collected by themultiple cookies may still be common. Accordingly, by correlatinginformation across the multiple tracking cookies, it may be ascertainedthat the multiple tracking cookies are associated with the same user.Further, in some embodiments, a threshold of correlation value may beestablished. Accordingly, the multiple tracking cookies may bedetermined to be associated with the user only if a correlation valueexceeds the threshold.

The platform may then apply the aforementioned algorithms to process thewebsites accessed by the devices and, in this way, profile the websitesas will be detailed below. The profiled website may then be used tocharacterize an individual who has been detected to access the profiledwebsite. Moreover, and as will be further detailed below, thecharacterized individual data may then be grouped along with otherindividuals' data assessed by the platform in a plurality of waysincluding, but not limited to, geographic, household, workplace,interests, affinities, gender, age, and the like.

It should be understood that each individual analyzed by the platform ofthe present disclosure may be weighted with an ‘affinity’ ofrelationship to a particular category. For example, for thoseindividuals who have visited websites profiled to be more ‘female’friendly may be determined, by the platform, to be most likely a‘female’ based on, either solely or at least in part, the individualsweb-traffic of profiled webpages associated with the individuals trackeddevice.

As yet a further example, the platform may identify individuals thatvisit webpages that include the words “cell phone” and determine thatthe individuals may be more likely to be shopping for cell phones.Further, by counting the number of times the individuals visit webpagesthat have predominately iPhones versus webpages that have predominatelyAndroid phones, the likelihood that such individuals prefer one phone tothe other may be assessed. The platform may group like users to createuseful statistical data. For example, the platform may create groups ofpeople that are most likely willing to purchase a specific product(e.g., cell phones, or, more specifically, Android smartphones).

Embodiments of the platform may further be used to enable a platformuser (e.g., mobile telecommunications company) to better understand itstarget market. Accordingly, data that has been acquired, aggregated, andprocessed by the platform may be provided to the user. For example anapplication program interface (API) may provide statistics about singleindividuals (e.g., likelihood that an individual prefers Android phonesto iPhones), or groups of individuals (e.g., which individuals preferAndroid phones to iPhones). Such statistics may be provided in, forexample, lists, charts, and graphs. Further, searchable and sortable rawdata may be provided. In some embodiments, the data may be provided tolicensed users. For example, users that have identified data such as,for example, AT&T, which has a list of known individuals, may use thedata to, for example, further market to their known list of individualsor predict churn.

In some embodiments, the processed data may be provided to the user as aplug-in. For example, if an individual logs into a website for the firsttime (e.g., Home Depot), the website owner may be able to customize thedisplay for the first-time individual. In other embodiments, theplatform may integrate with a customer relationship module (CRM). Inthis way, the CRM may be automatically updated with processed data forindividuals in the CRM.

Both the foregoing overview and the following detailed descriptionprovide examples and are explanatory only. Accordingly, the foregoingoverview and the following detailed description should not be consideredto be restrictive. Further, features or variations may be provided inaddition to those set forth herein. For example, embodiments may bedirected to various feature combinations and sub-combinations describedin the detailed description.

II. Platform Configuration

FIG. 1 illustrates one possible operating environment through which aplatform consistent with embodiments of the present disclosure may beprovided. By way of non-limiting example, a platform 100 may be hostedon a centralized server 110, such as, for example, a cloud computingservice. A user 105 may access platform 100 through a softwareapplication. The software application may be embodied as, for example,but not be limited to, a website, a web application, a desktopapplication, and a mobile application compatible with a computing device1600. One possible embodiment of the software application may beprovided by Clickagy, LLC.

As will be detailed with reference to FIG. 16 below, the computingdevice through which the platform may be accessed may comprise, but notbe limited to, for example, a desktop computer, laptop, a tablet, ormobile telecommunications device. Though the present disclosure iswritten with reference to a mobile telecommunications device, it shouldbe understood that any computing device may be employed to provide thevarious embodiments disclosed herein.

A user 105, such as, for example, an advertiser, may provide inputparameters to the platform. For example, input parameters may includefiltering criteria for identifying a plurality of sets of users, suchas, for example, an audience list and a sub-audience list based on userbehavior data. Additionally, the input parameters may also include aplurality of bid amounts corresponding to the plurality of sets ofusers. For example, the user 105 may provide a primary bid amountcorresponding to the audience list and a secondary bid amountcorresponding to the sub-audience list. Furthermore, the inputparameters may also include a contextual variable and a bid adjustmentparameter to indicate how bidding may be adjusted upon detection of thecontextual variable. The contextual variable specified by the user 105may include a domain on which a user is currently browsing, keywordspresent on the webpage being viewed by the user, affinities/importanceof the keywords to the user, time when the user is viewing the webpage,characteristics of the user device being used by the user and so on.

Information relevant to individuals associated with the inputparameters, such as, for example, which websites they visited, may besent to web crawler 115. Web crawler 115 may search webpages and onlinedocuments visited by individuals being tracked and gather dataassociated with the searched webpages and online documents. For example,web crawler 115 may utilize natural language processing and audio, videoand image processing to gather information for websites. Web crawler 115may further perform algorithms and build profiles based on webpages andonline documents being searched, such as, for example, constructing‘affinities’ for websites (further discussed below). Information andwebsite and online document profiles being tracked may be passed back toserver 110. Server 110 may further construct profiles for individualsbeing tracked and groups of individuals being tracked. The individualand group profiles as well as further data (e.g. personally identifiableinformation (PII), non-PII, de-identified data andwebsite/individual/group affinity) and bids may be returned to user 105.

Additionally, the platform 100 may be in communication with anad-exchange 120. The ad-exchange 120 may be, for example, a severcomputer, capable of communicating with the platform over acommunication network, such as the Internet. Further, the ad-exchange120 may facilitate a bidding based advertisement in collaboration with anumber of ad-servers (not shown in figure) and content servers. Forinstance, a content server may host a webpage on tips for buyingsmartphones. Accordingly, the content server may communicate to thead-exchange about the availability of an advertising space on thewebpage. In response, the ad-exchange may invite bids from multipleadvertisers (e.g. smartphone manufacturers) in order to present anadvertisement in the advertising space. Accordingly, ad-servers, such asthe platform 100, may communicate with the ad-exchange by providing abid amount in order to present advertisements. For instance, the user105 may be an administrator of a marketing campaign for a particularbrand of smartphones. Accordingly, the user 105 may communicate anaudience list and a sub-audience list and corresponding primary andsecondary bid amounts to the platform. In addition, the user 105 mayalso specify conditions under which the bidding may be performed. Inresponse, the platform may transmit bids to the ad-exchange 120 based onthe information received from the user 105. Accordingly, based on a win,advertisements selected by the user 105 may be presented to one or moreusers, such as those in the audience list and/or the sub-audience list.

III. Platform Operation

FIG. 2, FIG. 5 to FIG. 8 and FIG. 12 are flow charts setting forth thegeneral stages involved in methods 200, 500 to 800 and 1200 consistentwith various embodiment of the disclosure for providing a biddingplatform 100. Methods 200, 500 to 800 and 1200 may be implemented usinga computing device 1600 as described in more detail below with respectto FIG. 16.

Although methods 200, 500 to 800 and 1200 have been described to beperformed by platform 100, it should be understood that computing device1600 may be used to perform the various stages of methods 200, 500 to800 and 1200. Furthermore, in some embodiments, different operations maybe performed by different networked elements in operative communicationwith computing device 1600. For example, server 110 may be employed inthe performance of some or all of the stages in methods 200, 500 to 800and 1200. Moreover, server 110 may be configured much like computingdevice 1600.

Although the stages illustrated by the flow charts are disclosed in aparticular order, it should be understood that the order is disclosedfor illustrative purposes only. Stages may be combined, separated,reordered, and various intermediary stages may exist. Accordingly, itshould be understood that the various stages illustrated within the flowchart may be, in various embodiments, performed in arrangements thatdiffer from the ones illustrated. Moreover, various stages may be addedor removed from the flow charts without altering or deterring from thefundamental scope of the depicted methods and systems disclosed herein.Ways to implement the stages of methods 200, 500 to 800 and 1200 will bedescribed in greater detail below.

FIG. 3 further illustrates how logic functions may sort specific groupsof users. Specifically sorted groups may further enable users to targetindividuals in the proper context. For example, an individual searchingfor sports cars may receive advertisements for sports cars when lookingat websites related to cars, but not when looking at sports.Accordingly, an advertiser may specify a filtering criteria in terms ofuser behavior data in order to identify users who may be interested in aparticular product associated with the advertiser. Further, theadvertiser may have specified a logical combination of multiplefiltering criteria. For example, as illustrated in FIG. 3, theadvertiser may specify a logical “AND” combination of three keywordbased filtering criteria: “sports car”, “convertible” and “safety”.Further, the advertiser may specify logical “NOT” of a keyword basedfiltering criteria: “video games”. Accordingly, based on the keyword“sports car”, the platform may identify 650,000 users who are associatedwith user behavior data indicative of an interest in sports cars.Similarly, based on the keywords “convertible”, “safety” and “videogames”, 318,000, 85,000 and 1.8 million users respectively may beidentified. As per the logical expression specified by the advertiser, aresultant set of 23,000 users may be determined to be the group of usersto be targeted.

The 23,000 users thus identified may, in some instances, constitute theaudience list for marketing purposes. Accordingly, the advertiser mayspecify a bidding amount associated with the audience list. Accordingly,when a user in the audience list visits a webpage, the platform maydetect the presence of the user's identifier in the audience list andaccordingly bid for an advertising space on the webpage. For instance,the platform may transmit the bid amount to an ad-exchange incommunication with a webserver hosting the webpage. As a result, in theevent of a bid win, the advertiser may present a selected advertised tothe user viewing the webpage.

FIG. 4 illustrates and example 400 of provision of optimally targetedadvertisements in accordance with some embodiments. As illustrated, theplatform may initially identify the audience list of 23,000 usersinterested in buying sports cars based on filtering criteria provided bythe advertiser. Accordingly, the platform may be configured to trackonline activities of these users and present advertisements based onbidding.

However, in addition to specifying the bid amount, the platform mayenable the advertiser to specify one or more conditions under which thebid amount may be modified. For instance, as illustrated in FIG. 4, theadvertiser may specify a context corresponding to an advertisingopportunity within which the platform may bid for advertising spaces. Inthe example, the advertiser may specify presence of relevant keywords ona webpage being viewed by a user in order to determine bidding. Forinstance, when the user is viewing a webpage “sports.com” which does nothave any content related to car shopping, the platform may determine anirrelevant context. Accordingly, the platform may not bid foradvertising opportunities on “sports.com”. However, when the user isviewing the webpage “acmecars.com”, presence of a relevant keyword suchas “leasing” may trigger the platform to bid for advertisingopportunities on “acmecars.com”.

Further, in some embodiments, the platform may enable the advertiser toadditionally specify a bid adjustment parameter associated with the oneor more conditions. For instance, the advertiser may specify a nominalbid amount with the audience list for targeting users in the audiencelist independent of the webpage they may be currently viewing. Further,the advertiser may specify a modified bid amount to be used for biddingprovided one or more conditions specified by the advertiser are met. Forinstance, the advertiser may specify the modified bid amount, forexample a higher bid amount, to be used for bidding when a user in theaudience list is viewing a webpage containing certain relevant keywords.Accordingly, the platform may bid for an advertising space on thewebpage more aggressively in order to ensure that the likelihood of theadvertiser's advertisement being presented to the user increases.

Further, according to some embodiments, a computer implemented method,such as method 500, of bidding for advertisement opportunities based onuser behavior may be provided as illustrated in FIG. 5. An advertisementopportunity in general may correspond to any opportunity where a usermay be presented with an advertisement.

The presentation of the advertisement may take place in one or more formsuch as visual, auditory, tactile and so on. Accordingly, one or morepresentation devices such as, but not limited to, LED/LCD displaydevices, loud speakers and braille displays may be used to present theadvertisement. Further, in some instances the presentation device may bea public presentation device such as a roadside display device or anelectronic billboard. Alternatively, in some other instances, thepresentation device may be a personal presentation device such as, forexample, a laptop computer, a smartphone or a tablet computer.

In some instances, an advertisement opportunity may be in the form of anadvertising space within a content. For example, in case a user isviewing a webpage containing a news article, a portion of the webpagemay be reserved for presenting an advertisement. Accordingly, theportion of the webpage may constitute the advertisement opportunity.

Generally, content providers notify the availability of suchadvertisement opportunities to other interested entities such as forexample, advertisers, ad-servers and ad-exchanges. Accordingly, anadvertiser may bid for the advertisement opportunity in order to presenta desired advertisement to the user.

Accordingly, the platform of the present disclosure enables clients suchas advertisers to bid for advertisement opportunities based on userbehavior data. In some instances, the user behavior data may beindicative of interests of the user. Accordingly, in some instances, theplatform may be configured to monitor user behavior and create a userprofile indicating interests of the user. The user behavior may includefor example, present and/or past online activity performed by the usersuch as viewing webpages, online shopping, downloading content from theinternet, uploading content to the internet and interacting with adesktop application and/or a mobile application. An exemplary onlineuser behavior data based on which the user profile may be created isillustrated in FIG. 13.

Further, in some embodiments, data representing the user behavior may bede-identified. In other words, data representing the user behavior maynot include identifiable information such as name, phone number, postaladdress, bank account number and so on. Accordingly, privacy of usersmay be preserved. For instance, data representing the user behavior mayinclude a list of URLs visited by the user and a corresponding list oftimes when the user accessed the URLs.

In order to place bids based on user behavior data, the platform may beconfigured to receive a plurality of bid values corresponding to aplurality of sets of users corresponding to a plurality of filteringcriteria based on user behavior data. In other words, a user 105 of theplatform, such as an advertiser may specify a set of users based on afiltering criteria including characteristics of user behavior and acorresponding bid value to be used while bidding for presentingadvertisements to the set of users. For instance, the platform may beconfigured to identify a plurality of users constituting an audiencelist based on a primary filtering criteria. Similarly, the platform maybe configured to identify at least one user constituting a sub-audiencelist based on a secondary filtering criteria. Further, the platform maybe configured to associate different bid amounts with the audience listand the sub-audience list.

Accordingly, the method 500 may include a step 510 of receiving aprimary bid value associated with an audience list. The audience listmay include a plurality of users associated with user behavior datacorresponding to a primary filtering criteria. In other words, theaudience list may include user identifiers of users who may haveexhibited a certain user behavior in the past or are currentlyexhibiting such user behavior. For example, the advertiser may specifythe primary filtering criteria to be keywords and corresponding affinityvalues (e.g. “sports car”; >80% affinity) in order to identify theaudience list. Additionally, the advertiser may further specify a domainvisited by the users (e.g. www.topgear.com) to be used the secondaryfiltering criteria. Accordingly, the sub-audience list of users may becreated based on analysis of user behavior data of users present in theaudience list. Specifically, each user in the audience list who visitedthe domain specified in the secondary filtering criteria may beidentified and included in the sub-audience list. The sub-audience listmay present a set of users who may be more relevant to the advertiser.Therefore, the advertiser may specify a higher bid amount to be usedwhile bidding to present advertisements to the sub-audience.

Accordingly, the method 500 may include a step 520 of receiving asecondary bid value associated with the sub-audience list. Thesub-audience list may include one or more users associated with userbehavior data corresponding to the secondary filtering criteria.

Further, the method 500 may include a step 530 of transmitting a bid foran advertisement opportunity based on each of the primary bid value andthe secondary bid value. The bid may be transmitted to entities such as,for example, ad-exchanges, ad-servers and/or content servers.

For instance, the platform may transmit the bid in response to anotification of an advertisement opportunity provided by a contentserver. The notification may, in some instances, also indicate a currentcontext of the advertisement opportunity. For example, user identifierscorresponding to users currently viewing a webpage provided by thecontent server may be transmitted to the platform. Accordingly, theplatform may compare the user identifiers with those present in theaudience list and/or the sub-audience list.

When a user of the webpage is detected to be present in the audiencelist but not in the sub-audience list, the bid transmitted at step 530may include the primary bid value. However, when a user of the webpageis detected to be presented in each of the audience list and thesub-audience list, the bid transmitted at step 530 may include thesecondary bid value, which may be for example, higher or lower than theprimary bid value.

In order to create the audience list and/or the sub-audience list, theplatform may be configured to execute methods 600A and 600B asillustrated in FIG. 6A and FIG. 6B. Accordingly, the method may includea step 610 of receiving the primary filtering criteria for creating theaudience list. Further, the method may include a step 620 of filtering aset of users based on the primary filtering criteria. Further, each userin the set of users may be associated with user behavior data.Furthermore, the primary filtering criteria may be based on one or morecharacteristics of user behavior data as described earlier.Additionally, the method may include a step 630 of identifying theplurality of users from the set of users based on filtering of the setof users. The plurality of users may then constitute the audience list.

Likewise, the method may include a step 640 of receiving the secondaryfiltering criteria for creating the sub-audience list. Further, themethod may include a step 650 of filtering the plurality of users basedon the secondary filtering criteria. Furthermore, the method may includea step 660 of identifying one or more users in the audience list basedon filtering the plurality of users. The one or more users may thenconstitutes the sub-audience list.

According to some embodiments, prior to transmitting bids, the platformmay receive notifications from entities such as ad-exchanges about theavailability of advertisement opportunities. Accordingly, the platformmay be configured to execute methods 700 and 800 as illustrated in FIG.7 and FIG. 8.

Accordingly, the method may include a step 710 of receiving anotification of the advertisement opportunity from, for example, thead-exchange. Further, the notification may include a user identifiercorresponding to a user. For instance, a user associated with the useridentifier may be currently viewing a webpage on a content server.Accordingly, the content server may notify the ad-exchange of thepresence of an advertisement opportunity towards the user. In response,the ad-exchange may communicate the user identifier to the platform.

Further, the method may include a step 720 of detecting presence of theuser identifier in one or more of the audience list and the sub-audiencelist. Further, the method may include a step 730 of transmitting the bidfor the advertisement based on presence of the user identifier in one ormore of the audience list and the sub-audience list.

For example, when the user of the webpage is detected to be present inthe audience list but not in the sub-audience list, the bid transmittedmay include the primary bid value. For instance, the primary bid valuemay be a nominal value that the advertiser may be willing to spend inorder to target users in the audience list who may have a generalinterest towards a product/service associated with the advertiser.

However, when a user of the webpage is detected to be presented in eachof the audience list and the sub-audience list, the bid transmitted mayinclude the secondary bid value, which may be for example, higher orlower than the primary bid value. For instance, in case the sub-audiencerepresents users who have shown specific interest in theproduct/service, the advertiser may be willing to provide a higher bidamount in order to ensure that the user is exposed to an advertisementof the product/service.

Further, in some embodiments, the platform may also be configured toplace bids on current context associated with advertisementopportunities. Accordingly, as illustrated in FIG. 8, the method mayinclude a step 810 of receiving a notification of the advertisementopportunity along with a contextual variable. The contextual variablemay indicate one or more contextual conditions associated with theadvertisement opportunity. For example, the contextual variable mayindicate nature of content being viewed by the user,demographic/psychographic characteristics of the user, characteristicsof a user device through which the user is consuming the content, stateof the user device, sensor data obtained from the user device, currenttime, place, a physiological and/or psychological state of the userwhile consuming the content and so on.

Accordingly, the method may include a step 820 of evaluating thecontextual variable based on one or more of the primary filteringcriteria and the secondary filtering criteria. Further, each of theprimary filtering criteria and the secondary filtering criteria mayinclude a preferable contextual variable. For instance, the advertisermay have specified a primary filtering criteria as presence of certainrelevant keywords on a webpage being viewed by the user. Similarly, theadvertiser may have specified a secondary filtering criteria as OS type(e.g. Windows) executing on the user device used to view the webpage.Additionally, the method may include a step 830 may include a step oftransmitting the bid for the advertisement based on evaluation of thecontextual variable. For instance, if it is determined that a user iscurrently viewing an article related to iPhone through a Windows basedsmartphone, a desirable context of a windows phone user viewing contentabout iPhones may be detected. Accordingly, the advertiser may bid fordisplaying an advertisement for an iPhone to the user. Further, in someinstances, the bid amount transmitted may depend on evaluation of thecontextual variable in relation to the primary and the secondaryfiltering criteria. For instance, if the contextual variable satisfiesonly the primary filtering criteria (i.e. webpage relates to iPhones),the platform may place a nominal bid amount for presenting theadvertisement to the user. However, if the contextual variable satisfieseach of the primary filtering criteria and the secondary filteringcriteria (i.e. user using windows phone), the platform may place a bidmore aggressively by specifying a higher bid amount in order to ensurethat the likelihood of presenting the advertisement to the userincreases.

Further, in some embodiments, the platform may be configured to collectand maintain rich user behavior data by monitoring user behavior acrossmultiple websites. Accordingly, the platform may create and maintainuser profiles corresponding to different users. The user profiles maycontain, for example, keywords representing user interests and affinityvalues representing the importance of the keywords to correspondingusers.

In order to create the user profile, the method may include a step ofreceiving a plurality of Universal Resource Locators (URLs)corresponding to a plurality of webpages visited by the user. Further,the method may include a step of retrieving content from each of theplurality of webpages based on the plurality of URLs. For instance, acrawler program may be executed on a processor to automatically retrievecontent from each of the plurality of webpages by accessing theplurality of URLs.

Subsequent to retrieving the content, the method may include a step ofanalyzing the content from each of the plurality of webpages. In someembodiments, analyzing content from a webpage may include analyzingcontent corresponding to each content type present on the webpage. Forexample, both textual content and non-textual content such as audio,images, video and multimedia on the webpage may be analyzed.

Further, in some embodiments, the analyzing may include performingNatural Language Processing (NLP) of a textual content in the webpage.Additionally, in some embodiments, in case the webpage consists ofnon-textual content, a step of converting the non-textual content intotextual content may be performed. Subsequently, the NLP may be performedon the converted content.

For instance, as illustrated in FIG. 15, analyzing content of thewebpage using, for example, NLP may result in identification of acategory of content, such as “Entertainment”. Further, NLP may alsoidentify brand affinities of the webpage, such as for example, “Starwars” that may provide a greater contextual relevance and brandawareness to users. Additionally, NLP may also include event detectioninvolving identification of specific time-sensitive triggers, such asfor example, an upcoming “New Movie”. Further, NLP may also identifyimportant topics addressed in the content of the webpage and associatethose topics as concept tags with the webpage, such as for example,“Cinema”. Further, NLP may also include entity extraction involvingidentifying relevant proper nouns like people and/or brands.

Additionally, the method may include a step of identifying a pluralityof keywords corresponding to the webpage based on the analyzing. Forinstance, an exemplary set of keywords identified for a user based onthe user's interaction with various webpages is illustrated in FIG. 14.For example, based on the user's visiting of a webpage related to sportsnews, the keywords “Football” and “Basketball” may be identified andassociated with the user.

Furthermore, the plurality of keywords may be associated with aplurality of affinity values. The plurality of keywords and theplurality of affinity values may constitute the profile of the user. Forinstance, an affinity value of the keyword on a webpage may representhow strongly the content of the webpage relates to the keyword. In otherwords, the affinity value may represent a relative importance of thekeyword in the content. Accordingly, in some instances, keywords thatappear either in important sections of the webpage such as title,abstract, sub-headings, table of contents, index, main image and so onmay be associated with a relative larger affinity value as compared tothose keywords that appear elsewhere in the webpage. Likewise, keywordsthat appear often within the content of the webpage may be associatedwith a relatively larger affinity value as compared to those keywordsthat appear only once or a few times. Additionally, keywords that mayappear in different media types present on the webpage, such as text,image and audio/video may be associated with an even higher affinityvalue.

Further, in some embodiments, the method may further include a step ofdetermining an aggregated affinity value corresponding to a keywordbased on a first affinity value of the keyword corresponding to a firstwebpage and a second affinity value of the keyword corresponding to asecond webpage. In other words, the aggregated affinity value mayrepresent an overall affinity of the keyword to the user based on theuser's interaction with a plurality of webpages containing the keyword.

Further, in some embodiments, the aggregated affinity value may furtherbe based on a time decay value associated with each of the firstaffinity value and the second affinity value. For instance, each of thefirst affinity value and the second affinity value may be weighted basedon a time decay value. Accordingly, an impact of an affinity value onthe aggregated affinity value may be controlled according to forexample, a “freshness” associated with the affinity value. For instance,an affinity value of the keyword associated with a first webpage visiteda week ago may be weighted more than an affinity value of the keywordassociated with a second webpage visited a month ago.

An exemplary method 200 of creating user profiles based on user behaviordata in accordance with some embodiments, is illustrated in FIG. 2.Method 200 may begin at starting block 205 and proceed to stage 210where platform 100 may receive data from an individual's internet use.For example, the platform may receive information about a webpage thatthe individual visited or a Microsoft Word document or PDF that anindividual downloaded. Information may include the URL of the webpage.Further information may be received, including IP address of theindividual, search history of the individual, and geolocation of theindividual.

From stage 210, where platform 100 receives data from an individual'sInternet use, method 200 may advance to stage 220 where platform 100 mayfurther gather information associated with the individual's Internetuse. For example, the platform may crawl the webpage that the individualvisited. For example, the platform may search for specific key words orphrases. In some embodiments, if the webpage has already been crawled,the webpage may be skipped.

During the crawl, the platform may perform, for example, naturallanguage processing (NLP) to further process the context of the wordsand phrases in the text. In addition, the platform may utilize imagerecognition, audio recognition, and/or video recognition to gather dataabout the individual's Internet use. For example, image, video and audioinformation may be acquired from a webpage “www.example.com” to providethe individual's Internet use information. For instance, images may bescanned with optical character recognition (OCR). The OCR scanning maygenerate words or phrases for characterizing the webpage. Further, imagerecognition software may be used to characterize the webpage. Forexample, artificial intelligence (AI) software may be used to determinewhether an image is showing for example, a dog or a tree. Audio filesfrom the webpage may be scanned, using, for example, voice recognitionsoftware, to further provide information to characterize the webpage.Video files from a page may be converted to a series of images fromperiodic individual frames and scanned in the same manner as an image.In addition, the audio associated with the video files may be scanned toprovide data about the webpage. Likewise, text from the webpage may alsobe extracted and analyzed based on NLP. The combination of text, image,audio and video recognition may provide a human-style “view” of what thewebpage provides. The human-style “view” may enable the platform tooptimize characterization of the webpage.

Information that is acquired from the crawl may further be associatedwith how recently such information was associated with the webpage(e.g., newer information may be given a higher relevance than olderinformation). The platform may receive further information, for example,that is purchased from various data aggregators (e.g., aggregators thattrack specific IDs.) In addition, information may be tracked from anexisting individual base. For example, if the individual clicks (“IAgree”) on certain terms and conditions, the platform may place atracking cookie on the individual's device to further gatherinformation. In some embodiments, stages 210 and 220 may comprise 207,where platform 100 receives general data. The general data may include,for example, data from webpages (e.g., text, image, audio, and videodata associated with the webpage) and data from individuals (e.g., whichwebsites the individuals have visited, information from the individuals'social media profiles, and the like).

Once platform 100 further gathers information associated with theindividual's Internet use in stage 220, method 200 may continue to stage230 where platform 100 may analyze the information. In some embodiments,the platform may perform natural language processing (NLP) as well asimage, audio and video recognition to analyze the information. Forexample, the platform may use specific keywords and phrases, as well askeywords associated with image, video and audio files, found on eachwebpage and attach a plurality of ‘affinities’ to each page. Forexample, for a news article about iPhones, the platform may returnhundreds of ‘keywords’, including “Apple” with 94% affinity, “cellphone” with 81% affinity, and “screen” with 52% affinity. The platformmay then interpret the information based on the individual's Internetuse to create a profile associated with the affinities.

For example, an individual may visit a number of webpages that have highaffinity for keywords like “truck”, “football”, and “Scotch”. Such anindividual may be statistically more likely to be a male. As anotherexample, another individual may visit a number of webpages that havehigh affinity for keywords like “nail polish”, “Midol”, and “Pinterest.”Such an individual may be statistically more likely to be female. Suchstatistical predictions may be associated with a confidence level.Further, statistical predictions may be made for an abundance of othercharacteristics, such as, for example, but not limited to, age, maritalstatus, parental status, approximate household income, industry ofemployment, sport preference, automobile preference, and phonepreference.

After platform 100 analyzes the information for each individual in stage230, method 200 may proceed to stage 240 where platform 100 may groupusers based on certain characteristics. For example, individuals likelyto be of a certain characteristic, such as, for example, gender, age,marital status, parental status, approximate household income, andindustry of employment, may be grouped together. Additionally,individuals may be grouped together based on their preferences, such as,for example, sport preference, automobile preference, and phonepreference.

IV. Platform Architecture

The bidding platform 100 may be embodied as, for example, but not belimited to, a website, a web application, a desktop application, and amobile application compatible with a computing device. The computingdevice may comprise, but not be limited to, a desktop computer, laptop,a tablet, or mobile telecommunications device. Moreover, platform 100may be hosted on a centralized server, such as, for example, a cloudcomputing service. Although methods 200, 500 to 800 and 1200 have beendescribed to be performed by a computing device 1600, it should beunderstood that, in some embodiments, different operations may beperformed by different networked elements in operative communicationwith computing device 1600.

Embodiments of the present disclosure may comprise a system having amemory storage and a processing unit. The processing unit coupled to thememory storage, wherein the processing unit is configured to perform thestages of methods 200, 500 to 800 and 1200.

FIG. 16 is a block diagram of a system including computing device 1600.Consistent with an embodiment of the disclosure, the aforementionedmemory storage and processing unit may be implemented in a computingdevice, such as computing device 1600 of FIG. 16. Any suitablecombination of hardware, software, or firmware may be used to implementthe memory storage and processing unit. For example, the memory storageand processing unit may be implemented with computing device 1600 or anyof other computing devices 1618, in combination with computing device1600. The aforementioned system, device, and processors are examples andother systems, devices, and processors may comprise the aforementionedmemory storage and processing unit, consistent with embodiments of thedisclosure.

With reference to FIG. 16, a system consistent with an embodiment of thedisclosure may include a computing device, such as computing device1600. In a basic configuration, computing device 1600 may include atleast one processing unit 1602 and a system memory 1604. Depending onthe configuration and type of computing device, system memory 1604 maycomprise, but is not limited to, volatile (e.g. random access memory(RAM)), non-volatile (e.g. read-only memory (ROM)), flash memory, or anycombination. System memory 1604 may include operating system 1605, oneor more programming modules 1606, and may include a program data 1607.Operating system 1605, for example, may be suitable for controllingcomputing device 1600's operation. In one embodiment, programmingmodules 1606 may include affinity calculating modules, such as, forexample, webpage affinity calculation application 1620. Furthermore,embodiments of the disclosure may be practiced in conjunction with agraphics library, other operating systems, or any other applicationprogram and is not limited to any particular application or system. Thisbasic configuration is illustrated in FIG. 16 by those components withina dashed line 1608.

Computing device 1600 may have additional features or functionality. Forexample, computing device 1600 may also include additional data storagedevices (removable and/or non-removable) such as, for example, magneticdisks, optical disks, or tape. Such additional storage is illustrated inFIG. 16 by a removable storage 1609 and a non-removable storage 1610.Computer storage media may include volatile and nonvolatile, removableand non-removable media implemented in any method or technology forstorage of information, such as computer readable instructions, datastructures, program modules, or other data. System memory 1604,removable storage 1609, and non-removable storage 1610 are all computerstorage media examples (i.e., memory storage.) Computer storage mediamay include, but is not limited to, RAM, ROM, electrically erasableread-only memory (EEPROM), flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, magneticcassettes, magnetic tape, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to storeinformation and which can be accessed by computing device 1600. Any suchcomputer storage media may be part of device 1600. Computing device 1600may also have input device(s) 1612 such as a keyboard, a mouse, a pen, asound input device, a touch input device, etc. Output device(s) 1614such as a display, speakers, a printer, etc. may also be included. Theaforementioned devices are examples and others may be used.

Computing device 1600 may also contain a communication connection 1616that may allow device 1600 to communicate with other computing devices1618, such as over a network in a distributed computing environment, forexample, an intranet or the Internet. Communication connection 1616 isone example of communication media. Communication media may typically beembodied by computer readable instructions, data structures, programmodules, or other data in a modulated data signal, such as a carrierwave or other transport mechanism, and includes any information deliverymedia. The term “modulated data signal” may describe a signal that hasone or more characteristics set or changed in such a manner as to encodeinformation in the signal. By way of example, and not limitation,communication media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, radiofrequency (RF), infrared, and other wireless media. The term computerreadable media as used herein may include both storage media andcommunication media.

As stated above, a number of program modules and data files may bestored in system memory 1604, including operating system 1605. Whileexecuting on processing unit 1602, programming modules 1606 (e.g.,platform application 1620) may perform processes including, for example,one or more of methods 200, 500 to 800 and 1200's stages as describedabove. The aforementioned process is an example, and processing unit1602 may perform other processes. Other programming modules that may beused in accordance with embodiments of the present disclosure mayinclude electronic mail and contacts applications, word processingapplications, spreadsheet applications, database applications, slidepresentation applications, drawing or computer-aided applicationprograms, etc.

Generally, consistent with embodiments of the disclosure, programmodules may include routines, programs, components, data structures, andother types of structures that may perform particular tasks or that mayimplement particular abstract data types. Moreover, embodiments of thedisclosure may be practiced with other computer system configurations,including hand-held devices, multiprocessor systems,microprocessor-based or programmable consumer electronics,minicomputers, mainframe computers, and the like. Embodiments of thedisclosure may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications network. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

Furthermore, embodiments of the disclosure may be practiced in anelectrical circuit comprising discrete electronic elements, packaged orintegrated electronic chips containing logic gates, a circuit utilizinga microprocessor, or on a single chip containing electronic elements ormicroprocessors. Embodiments of the disclosure may also be practicedusing other technologies capable of performing logical operations suchas, for example, AND, OR, and NOT, including but not limited tomechanical, optical, fluidic, and quantum technologies. In addition,embodiments of the disclosure may be practiced within a general purposecomputer or in any other circuits or systems.

Embodiments of the disclosure, for example, may be implemented as acomputer process (method), a computing system, or as an article ofmanufacture, such as a computer program product or computer readablemedia. The computer program product may be a computer storage mediareadable by a computer system and encoding a computer program ofinstructions for executing a computer process. The computer programproduct may also be a propagated signal on a carrier readable by acomputing system and encoding a computer program of instructions forexecuting a computer process. Accordingly, the present disclosure may beembodied in hardware and/or in software (including firmware, residentsoftware, micro-code, etc.). In other words, embodiments of the presentdisclosure may take the form of a computer program product on acomputer-usable or computer-readable storage medium havingcomputer-usable or computer-readable program code embodied in the mediumfor use by or in connection with an instruction execution system. Acomputer-usable or computer-readable medium may be any medium that cancontain, store, communicate, propagate, or transport the program for useby or in connection with the instruction execution system, apparatus, ordevice.

The computer-usable or computer-readable medium may be, for example butnot limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, device, or propagationmedium. More specific computer-readable medium examples (anon-exhaustive list), the computer-readable medium may include thefollowing: an electrical connection having one or more wires, a portablecomputer diskette, a random access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, and a portable compact disc read-only memory(CD-ROM). Note that the computer-usable or computer-readable mediumcould even be paper or another suitable medium upon which the program isprinted, as the program can be electronically captured, via, forinstance, optical scanning of the paper or other medium, then compiled,interpreted, or otherwise processed in a suitable manner, if necessary,and then stored in a computer memory.

Embodiments of the present disclosure, for example, are described abovewith reference to block diagrams and/or operational illustrations ofmethods, systems, and computer program products according to embodimentsof the disclosure. The functions/acts noted in the blocks may occur outof the order as shown in any flowchart. For example, two blocks shown insuccession may in fact be executed substantially concurrently or theblocks may sometimes be executed in the reverse order, depending uponthe functionality/acts involved.

While certain embodiments of the disclosure have been described, otherembodiments may exist. Furthermore, although embodiments of the presentdisclosure have been described as being associated with data stored inmemory and other storage mediums, data can also be stored on or readfrom other types of computer-readable media, such as secondary storagedevices, like hard disks, solid state storage (e.g., USB drive), or aCD-ROM, a carrier wave from the Internet, or other forms of RAM or ROM.Further, the disclosed methods' stages may be modified in any manner,including by reordering stages and/or inserting or deleting stages,without departing from the disclosure.

All rights including copyrights in the code included herein are vestedin and the property of the Applicant. The Applicant retains and reservesall rights in the code included herein, and grants permission toreproduce the material only in connection with reproduction of thegranted patent and for no other purpose.

V. Aspect

The application includes at least the following aspects:

Aspect 1. A method of bidding for advertisement opportunities, whereinthe method is computer implemented, the method comprising:

a. receiving a primary bid value associated with an audience list,wherein the audience list comprises a plurality of users, wherein theplurality of users is associated with user behavior data correspondingto a primary filtering criteria;

b. receiving a secondary bid value associated with a sub-audience list,wherein the sub-audience list comprises at least one user, wherein theat least one user is associated with user behavior data corresponding toa secondary filtering criteria; and

c. transmitting a bid for an advertisement opportunity based on each ofthe primary bid value and the secondary bid value, wherein theadvertisement is presentable to the at least one user.

Aspect 2. The method of aspect 1, wherein each of the primary filteringcriteria and the secondary filtering criteria is based on at least onecharacteristic of user behavior data.

Aspect 3. The method of aspect 2, wherein the user behavior datacomprises data corresponding to online activity performed by a user,wherein the at least one characteristic comprises at least one of adomain name, a webpage identifier, a keyword, an affinity value of thekeyword, a demographic variable, a psychographic variable, device dataand a time corresponding to capture of the user behavior data.

Aspect 4. The method of aspect 3, wherein, the device data comprises atleast one of a device identifier associated with a user device,indicator of screen size of the user device, a network identifierassociated with a communication network used for performing the onlineactivity, an Internet Service Provider (ISP) associated with thecommunication network, an Operating System (OS) identifier of an OSinstalled on the user device and a browser identifier of a browserinstalled on the user device.

Aspect 5. The method of aspect 1, wherein the primary bid valuecomprises a default bid amount, wherein the secondary bid valuecomprises an adjustment value, wherein the bid for the advertisementcomprises a transmitted bid value obtained by adjusting the default bidamount according to the adjustment value.

Aspect 6. The method of aspect 1 further comprising:

a. receiving the primary filtering criteria for creating the audiencelist;

b. filtering a set of users based on the primary filtering criteria,wherein each user in the set of users is associated with user behaviordata, wherein the primary filtering criteria is based on at least onecharacteristic of user behavior data; and

c. identifying the plurality of users from the set of users based onfiltering of the set of users, wherein the plurality of usersconstitutes the audience list.

Aspect 7. The method of aspect 1 further comprising:

a. receiving the secondary filtering criteria for creating thesub-audience list;

b. filtering the plurality of users based on the secondary filteringcriteria; and

c. identifying at least one user in the audience list based on filteringthe plurality of users, wherein the at least one user constitutes thesub-audience list.

Aspect 8. The method of aspect 1, wherein the secondary bid value isbased on a contextual variable associated with the advertisementopportunity.

Aspect 9. The method of aspect 1 further comprising:

a. receiving a notification of the advertisement opportunity, whereinthe notification comprises the contextual variable; and

b. evaluating the contextual variable based on at least one of theprimary filtering criteria and the secondary filtering criteria, whereineach of the primary filtering criteria and the secondary filteringcriteria comprises a preferable contextual variable, whereintransmitting the bid for the advertisement is further based onevaluation of the contextual variable.

Aspect 10. The method of aspect 1 further comprising:

a. receiving a notification of the advertisement opportunity, whereinthe notification comprises a user identifier corresponding to a user;and

b. detecting presence of the user identifier in at least one of theaudience list and the sub-audience list, wherein transmitting the bidfor the advertisement is based further on presence of the useridentifier in at least one of the audience list and the sub-audiencelist.

Aspect 11. The method of aspect 10, wherein the bid for theadvertisement comprises the primary bid value, wherein the useridentifier is present in the audience list and absent in thesub-audience list.

Aspect 12. The method of aspect 10, wherein the bid for theadvertisement comprises the secondary bid value, wherein the useridentifier is present in each of the audience list and the sub-audiencelist.

Aspect 13. The method of aspect 1, wherein the secondary bid valuecomprises one of a positive value and a negative value, wherein the bidfor the advertisement is transmitted provided the secondary bid value isa positive value.

Aspect 14. The method of aspect 1 further comprising:

a. receiving a notification of a win based on the bid; and

b. transmitting the advertisement to the at least one user based onreceiving the notification of the win.

Aspect 15. The method of aspect 1, wherein the user behavior data isanonymous.

Aspect 16. A system for facilitating bidding of advertisementopportunities, wherein the system comprises a communication module and aprocessing module communication module coupled to the communicationmodule, wherein the communication module is configured to:

a. receive a primary bid value associated with an audience list, whereinthe audience list comprises a plurality of users, wherein the pluralityof users is associated with user behavior data corresponding to aprimary filtering criteria;

b. receive a secondary bid value associated with a sub-audience list,wherein the sub-audience list comprises at least one user, wherein theat least one user is associated with user behavior data corresponding toa secondary filtering criteria; and

c. transmit a bid for an advertisement opportunity based on each of theprimary bid value and the secondary bid value, wherein the advertisementis presentable to the at least one user.

Aspect 17. The system of aspect 16, wherein each of the primaryfiltering criteria and the secondary filtering criteria is based on atleast one characteristic of user behavior data.

Aspect 18. The system of aspect 17, wherein the user behavior datacomprises data corresponding to online activity performed by a user,wherein the at least one characteristic comprises at least one of adomain name, a webpage identifier, a keyword, an affinity value of thekeyword, a demographic variable, a psychographic variable, device dataand a time corresponding to capture of the user behavior data.

Aspect 19. The system of aspect 18, wherein, the device data comprisesat least one of a device identifier associated with a user device,indicator of screen size of the user device, a network identifierassociated with a communication network used for performing the onlineactivity, an Internet Service Provider (ISP) associated with thecommunication network, an Operating System (OS) identifier of an OSinstalled on the user device and a browser identifier of a browserinstalled on the user device.

Aspect 20. The system of aspect 16, wherein the primary bid valuecomprises a default bid amount, wherein the secondary bid valuecomprises an adjustment value, wherein the bid for the advertisementcomprises a transmitted bid value obtained by adjusting the default bidamount according to the adjustment value.

Aspect 21. The system of aspect 16, wherein the communication module isfurther configured to receive the primary filtering criteria forcreating the audience list, wherein the processing module is configuredto:

a. filter a set of users based on the primary filtering criteria,wherein each user in the set of users is associated with user behaviordata, wherein the primary filtering criteria is based on at least onecharacteristic of user behavior data; and

b. identify the plurality of users from the set of users based onfiltering of the set of users, wherein the plurality of usersconstitutes the audience list.

Aspect 22. The system of aspect 16, wherein the communication module isfurther configured to receive the secondary filtering criteria forcreating the sub-audience list, wherein the processing module isconfigured to:

a. filter the plurality of users based on the secondary filteringcriteria; and

b. identify at least one user in the audience list based on filteringthe plurality of users, wherein the at least one user constitutes thesub-audience list.

Aspect 23. The system of aspect 16, wherein the secondary bid value isbased on a contextual variable associated with the advertisementopportunity.

Aspect 24. The system of aspect 16, wherein the communication module isfurther configured to receive a notification of the advertisementopportunity, wherein the notification comprises the contextual variable,wherein the processing module is configured to evaluate the contextualvariable based on at least one of the primary filtering criteria and thesecondary filtering criteria, wherein each of the primary filteringcriteria and the secondary filtering criteria comprises a preferablecontextual variable, wherein transmitting the bid for the advertisementis further based on evaluation of the contextual variable.

Aspect 25. The system of aspect 16, wherein the communication module isfurther configured to receive a notification of the advertisementopportunity, wherein the notification comprises a user identifiercorresponding to a user, wherein the processing module is configured todetect presence of the user identifier in at least one of the audiencelist and the sub-audience list, wherein transmitting the bid for theadvertisement is based further on presence of the user identifier in atleast one of the audience list and the sub-audience list.

Aspect 26. The system of aspect 25, wherein the bid for theadvertisement comprises the primary bid value, wherein the useridentifier is present in the audience list and absent in thesub-audience list.

Aspect 27. The system of aspect 25, wherein the bid for theadvertisement comprises the secondary bid value, wherein the useridentifier is present in each of the audience list and the sub-audiencelist.

Aspect 28. The system of aspect 16, wherein the secondary bid valuecomprises one of a positive value and a negative value, wherein the bidfor the advertisement is transmitted provided the secondary bid value isa positive value.

Aspect 29. The system of aspect 16, wherein the communication module isfurther configured to:

a. receive a notification of a win based on the bid; and

b. transmit the advertisement to the at least one user based onreceiving the notification of the win.

Aspect 30. The system of aspect 16, wherein the user behavior data isanonymous.

VI. Claims

While the specification includes examples, the disclosure's scope isindicated by the following claims. Furthermore, while the specificationhas been described in language specific to structural features and/ormethodological acts, the claims are not limited to the features or actsdescribed above. Rather, the specific features and acts described aboveare disclosed as example for embodiments of the disclosure.

Insofar as the description above and the accompanying drawing discloseany additional subject matter that is not within the scope of the claimsbelow, the disclosures are not dedicated to the public and the right tofile one or more applications to claims such additional disclosures isreserved.

The following is claimed:
 1. A method of bidding for advertisementopportunities, wherein the method is computer implemented, the methodcomprising: receiving a specification of a first audience list based onprimary filtering criteria, wherein the first audience list comprises aplurality of users having user behavior data corresponding to theprimary filtering criteria; receiving a primary bid value associatedwith the first audience list; specifying an adjustment to the primarybid value based on at least one contextual variable, wherein specifyingthe adjustment to the primary bid value based on the at least onecontextual variable comprises: specifying an adjustment filteringcriteria, wherein specifying the adjustment filtering criteria comprisesreceiving a selection of the following: an adjustment filter type and anadjustment filter parameter, wherein receiving the selection theadjustment filter type comprises receiving a selection of at least onefrom a group consisting of the following: an additional audience listbased adjustment, keyword based adjustment, affinity based adjustment,web traffic history based adjustment, location based adjustment, andtime based adjustment; wherein receiving the selection the adjustmentfilter parameter comprises receiving a selection of at least one from agroup consisting of the following: at least one additional audiencelist, at least one keyword, at least one interest, at least one webdomain, at least one web page, at least one location, and at least onetime, and specifying an adjustment value to be applied to the primarybid value when the at least one contextual variable is detected inassociation with an advertisement opportunity, wherein specifying theadjustment value comprises receiving a specification of the following: apercentage of adjustment for the primary bid value, wherein thepercentage is configured to be either of a positive value and a negativevalue, wherein an adjustment to the positive value indicates amultiplier to the primary bid value, and wherein an adjustment to thenegative value indicates a divider of the primary bid value; tracking anonline behavior of the plurality of users in the first audience list todetect the at least one contextual variable, wherein tracking the onlinebehavior of the plurality of users comprises: accessing a web page thathas been accessed by a user of the plurality of users, performingnatural language processing on the web page to determine keywordsassociated with the web page, and associating the keywords with theuser; and transmitting a bid for the advertisement opportunity, whereintransmitting the bid for the advertisement opportunity comprises:calculating the bid, when the at least one contextual variable isdetected in association with the advertisement opportunity, by adjustingthe primary bid value by the following: the multiplier associated withthe adjustment value when the adjustment value is positive for the atleast one contextual variable, and the divider associated with theadjustment value when the adjustment value is negative for the at leastone contextual variable.
 2. The method of claim 1, wherein each of theprimary filtering criteria and the adjustment filtering criteria isbased on at least one characteristic of user behavior data.
 3. Themethod of claim 2, wherein the user behavior data comprises datacorresponding to online activity performed by a user, wherein the atleast one characteristic comprises at least one of the following: adomain name, a webpage identifier, a keyword, an affinity value of thekeyword, a demographic variable, a psychographic variable, device dataand a time corresponding to capture of the user behavior data.
 4. Themethod of claim 3, wherein, the device data comprises at least one ofthe following: a device identifier associated with a user device,indicator of screen size of the user device, a network identifierassociated with a communication network used for performing the onlineactivity, an Internet Service Provider (ISP) associated with thecommunication network, an Operating System (OS) identifier of an OSinstalled on the user device and a browser identifier of a browserinstalled on the user device.
 5. The method of claim 1, furthercomprising: establishing a profile for the user, wherein the profilecomprises the keywords; determining the user profile satisfies the atleast one contextual variable; and adding the user to a second audiencelist upon a determination that the at least one contextual variable issatisfied.
 6. The method of claim 1, further comprising: receiving anotification of the advertisement opportunity, wherein the advertisementopportunity is associated with the user having profile data satisfyingthe at least one contextual variable; and evaluating the at least onecontextual variable based on at least one of the primary filteringcriteria and the adjustment filtering criteria, wherein each of theprimary filtering criteria and the adjustment filtering criteriacomprises a preferable contextual variable, wherein transmitting the bidfor the advertisement is further based on evaluation of the preferablecontextual variable.
 7. The method of claim 6, further comprising:receiving a notification of the advertisement opportunity, wherein thenotification comprises a user identifier corresponding to the user; anddetecting presence of the user identifier in at least one of the firstaudience list and the second list, wherein transmitting the bid for theadvertisement is based further on presence of the user identifier in atleast one of the first audience list and the second list.
 8. The methodof claim 10, wherein the bid for the advertisement comprises the primarybid value-when the user identifier is present in the first audience listand absent in the second audience list.
 9. The method of claim 10,wherein the bid for the advertisement comprises the secondary bid value,wherein the user identifier is present in each of the first audiencelist and the second audience list.
 10. The method of claim 1, furthercomprising: receiving a notification of a win based on the bid; andtransmitting the advertisement to be based on receiving the notificationof the win.
 11. The method of claim 1, wherein the user behavior data isanonymous.
 12. A method of bidding for advertisement opportunities,wherein the method is computer implemented, the method comprising:creating an audience list, wherein creating the first audience listcomprises: receiving primary filtering criteria for creating the firstaudience list; filtering a set of users based on the primary filteringcriteria, wherein each user in the set of users is associated with userbehavior data, wherein the primary filtering criteria is based on atleast one characteristic of user behavior data; and identifying aplurality of users from the set of users based on filtering of the setof users, wherein the plurality of users constitutes the first audiencelist; receiving a primary bid value associated with the first audiencelist; specifying a first adjustment to the primary bid value based on afirst contextual variable, wherein specifying the adjustment to theprimary bid value based on the first contextual variable comprises:specifying an adjustment filtering criteria, wherein specifying theadjustment filtering criteria comprises receiving a selection of thefollowing: an adjustment filter type and an adjustment filter parameter,wherein receiving the selection the adjustment filter type comprisesreceiving a selection of at least one from a group consisting of thefollowing: an additional audience list based adjustment, keyword basedadjustment, affinity based adjustment, web traffic history basedadjustment, location based adjustment, and time based adjustment;wherein receiving the selection the adjustment filter parametercomprises receiving a selection of at least one from a group consistingof the following: at least one additional audience list, at least onekeyword, at least one interest, at least one web domain, at least oneweb page, at least one location, and at least one time, and specifyingan adjustment value to be applied to the primary bid value when thefirst contextual variable is detected in association with anadvertisement opportunity, wherein specifying the adjustment valuecomprises receiving a specification of the following: a slider forspecifying a percentage by which to adjust the primary bid value,wherein slider is configured to be manipulated in a positive directionand negative direction such that the percentage can be specified as apositive value and a negative value, wherein an adjustment to thepositive value indicates a multiplier to the primary bid value, andwherein an adjustment to the negative value indicates a divider of theprimary bid value; specifying a second adjustment to the primary bidvalue based on a second contextual variable, wherein specifying theadjustment to the primary bid value based on the second contextualvariable comprises: specifying an additional adjustment filteringcriteria, wherein specifying the additional adjustment filteringcriteria comprises receiving a selection of the following: an additionaladjustment filter type and an additional adjustment filter parameter,and specifying an additional adjustment value to be applied to theprimary bid value when the second contextual variable is detected;specifying logical operations for combining the first adjustment and thesecond adjustment to the primary bid value when calculating a bid;tracking an online behavior of the plurality of users in the firstaudience list to detect one of the following: the first contextualvariable and the second contextual variable, wherein tracking the onlinebehavior of the plurality of users comprises: accessing a web page thathas been accessed by a user of the plurality of users, performingnatural language processing on the web page to determine keywordsassociated with the web page, associating the keywords with the user,and establishing a profile for the user; and receiving a notification ofthe advertisement opportunity, wherein the advertisement opportunitycorresponds to the user having the profile that satisfies at least oneof the following: the first contextual variable and the secondcontextual variable; transmitting the bid for the advertisementopportunity, wherein transmitting the bid for the advertisementopportunity comprises: calculating the bid, when the first contextualvariable and the second contextual variable are detected in associationwith the advertisement opportunity, by adjusting the primary bid valueby the adjustment value and the additional adjustment value inaccordance to the logical operations specified for combining the firstadjustment and the second adjustment.
 13. The method of claim 12,wherein transmitting the bid for the advertisement opportunity furthercomprises: calculating the bid, when only the first contextual variableis detected in association with the advertisement opportunity, byadjusting the primary bid value by the following: the multiplierassociated with the adjustment value when the adjustment value ispositive for the at least one contextual variable, and the dividerassociated with the adjustment value when the adjustment value isnegative for the at least one contextual variable.
 14. The method ofclaim 12, wherein transmitting the bid for the advertisement opportunityfurther comprises: calculating the bid, when only the second contextualvariable is detected in association with the advertisement opportunity,by adjusting the primary bid value by the following: the multiplierassociated with the additional adjustment value when the adjustmentvalue is positive for the at least one contextual variable, and thedivider associated with the additional adjustment value when theadjustment value is negative for the at least one contextual variable.15. A method of bidding for advertisement opportunities, wherein themethod is computer implemented, the method comprising: creating a firstaudience list, wherein creating the first audience list comprises:receiving primary filtering criteria for creating the first audiencelist; filtering a set of users based on the primary filtering criteria,wherein each user in the set of users is associated with user behaviordata, wherein the primary filtering criteria is based on at least onecharacteristic of user behavior data; and identifying a plurality ofusers from the set of users based on filtering of the set of users,wherein the plurality of users constitutes the first audience list;receiving a primary bid value associated with the first audience list;specifying a first adjustment to the primary bid value based on a firstcontextual variable, wherein specifying the adjustment to the primarybid value based on the first contextual variable comprises: specifyingan adjustment filtering criteria, wherein specifying the adjustmentfiltering criteria comprises receiving a selection of the following: anadjustment filter type and an adjustment filter parameter, whereinreceiving the selection the adjustment filter type comprises receiving aselection of at least one from a group consisting of the following: anadditional audience list based adjustment, keyword based adjustment,affinity based adjustment, web traffic history based adjustment,location based adjustment, and time based adjustment; wherein receivingthe selection the adjustment filter parameter comprises receiving aselection of at least one from a group consisting of the following: atleast one additional audience list, at least one keyword, at least oneinterest, at least one web domain, at least one web page, at least onelocation, and at least one time, and specifying an adjustment value tobe applied to the primary bid value when the first contextual variableis detected in association with an advertisement opportunity, whereinspecifying the adjustment value comprises receiving a specification ofthe following: a slider for specifying a percentage by which to adjustthe primary bid value, wherein slider is configured to be manipulated ina positive direction and negative direction such that the percentage canbe specified as a positive value and a negative value, wherein anadjustment to the positive value indicates a multiplier to the primarybid value, and wherein an adjustment to the negative value indicates adivider of the primary bid value, and a blacklist indicator, wherein theblacklist indicator, upon activation, is configured to set the primarybid value to zero; specifying a second adjustment to the primary bidvalue based on a second contextual variable, wherein specifying theadjustment to the primary bid value based on the second contextualvariable comprises: specifying an additional adjustment filteringcriteria, wherein specifying the additional adjustment filteringcriteria comprises receiving a selection of the following: an additionaladjustment filter type and an additional adjustment filter parameter,and specifying an additional adjustment value to be applied to theprimary bid value when the second contextual variable is detected;tracking an online behavior of the plurality of users in the firstaudience list to detect the first contextual variable, wherein trackingthe online behavior of the plurality of users comprises: accessing a webpage that has been accessed by a user of the plurality of users,performing natural language processing on the web page to determinekeywords associated with the web page, associating the keywords with theuser, establishing a profile for the user based on, at least in part,the keywords, associating the user with the first contextual variablewhen profile data for the user corresponds to the first contextualvariable, and associating the user with the second contextual variablewhen profile data for the user corresponds to the first contextualvariable; and generating a second audience list having users associatedwith the first contextual variable; generating a third audience listhaving users associated with the second contextual variable; specifyinglogical operations for combining the first adjustment and the secondadjustment to the primary bid value; receiving a notification of theadvertisement opportunity, wherein the advertisement opportunitycomprises a user identifier; determining whether the user identifier isassociated with a user at least one of the following: the first audiencelist, the second audience list, and the third audience list;transmitting the bid for the advertisement opportunity, whereintransmitting the bid for the advertisement opportunity comprises:calculating the bid, when the user identifier is associated with thefirst audience list, the second audience list, and the third audiencelist, by adjusting the primary bid value by the adjustment value and theadditional adjustment value in accordance to the logical operationsspecified for combining the first adjustment and the second adjustment.16. The method of claim 15, wherein specifying logical operations forcombining the first adjustment and the second adjustment to the primarybid value when calculating the comprises graphical user interfacemanipulations within a workspace, wherein the graphical user interfacemanipulations correspond to at least one of the following: dragging anddropping of at least one audience list, and generating an overlap in aVenn diagram like workspace interface.